cudf.DataFrame.memory_usage

DataFrame.memory_usage(index: bool = True, deep: bool = False) → Series

Return the memory usage of the DataFrame.

Parameters:

indexbool, default True

Specifies whether to include the memory usage of the index.

deepbool, default False

The deep parameter is ignored and is only included for pandas compatibility.

Returns:

Series

A Series whose index is the original column names and whose values is the memory usage of each column in bytes.

Examples

>>> import cudf
>>> import numpy as np
>>> dtypes = [int, float, str, bool]
>>> data = {typ.__name__: [typ(1)] * 5000 for typ in dtypes}
>>> df = cudf.DataFrame(data)
>>> df.head()
int  float str  bool
0    1    1.0   1  True
1    1    1.0   1  True
2    1    1.0   1  True
3    1    1.0   1  True
4    1    1.0   1  True
>>> df.memory_usage(index=False)
int      40000
float    40000
str      25004
bool      5000
dtype: int64

Use a Categorical for efficient storage of an object-dtype column with many repeated values.

>>> df['str'].astype('category').memory_usage(deep=True)
5009